Version 1
: Received: 29 March 2024 / Approved: 30 March 2024 / Online: 1 April 2024 (17:05:16 CEST)
How to cite:
Wang, R.; Ling, M.; Xu, L.; Shao, J. Research on Unmanned Vehicle Path Planning Based on Improved A and DWA Fusion Algorithm. Preprints2024, 2024040037. https://doi.org/10.20944/preprints202404.0037.v1
Wang, R.; Ling, M.; Xu, L.; Shao, J. Research on Unmanned Vehicle Path Planning Based on Improved A and DWA Fusion Algorithm. Preprints 2024, 2024040037. https://doi.org/10.20944/preprints202404.0037.v1
Wang, R.; Ling, M.; Xu, L.; Shao, J. Research on Unmanned Vehicle Path Planning Based on Improved A and DWA Fusion Algorithm. Preprints2024, 2024040037. https://doi.org/10.20944/preprints202404.0037.v1
APA Style
Wang, R., Ling, M., Xu, L., & Shao, J. (2024). Research on Unmanned Vehicle Path Planning Based on Improved A and DWA Fusion Algorithm. Preprints. https://doi.org/10.20944/preprints202404.0037.v1
Chicago/Turabian Style
Wang, R., Li Xu and Jiaping Shao. 2024 "Research on Unmanned Vehicle Path Planning Based on Improved A and DWA Fusion Algorithm" Preprints. https://doi.org/10.20944/preprints202404.0037.v1
Abstract
Traditional path planning algorithms have drawbacks in unmanned vehicle path planning, such as low efficiency, unsuitability for dynamic environments, and high computational complexity. To address these issues, this paper proposes a dynamic path planning method for unmanned vehicles that combines the A* algorithm with an improved Dynamic Window Approach (DWA). Firstly, a collision risk function is incorporated into the DWA algorithm to evaluate scores based on the braking distance corresponding to the current vehicle speed and the distance to obstacles, determining whether timely braking or obstacle avoidance is feasible. The earlier the braking or obstacle avoidance, the higher the score, thereby enhancing the evaluation algorithm of the DWA. Secondly, the key points extracted by the A* algorithm are used as temporary target points for the DWA algorithm, reducing the eight search directions to five. Finally, the key points of the A* algorithm’s path are set as the temporary end points for the DWA algorithm, integrating the two algorithms to plan a smoother curved path that can avoid unknown and dynamic obstacles. Simulation data demonstrates that compared to traditional artificial potential field methods, RRT, and Dijkstra’s algorithm, the new fusion algorithm reduces time by 11.71 seconds, a decrease of 6.9%, and shortens path length by 3.093 meters, a reduction of 4.16%, while increasing average linear velocity by 2.9%. The proposed algorithm ensures smoother paths while also making paths more rational and efficient
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
